Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass.
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Mass; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Mass.
J Allergy Clin Immunol. 2018 Apr;141(4):1250-1258. doi: 10.1016/j.jaci.2017.05.052. Epub 2017 Jul 20.
Variations in drug response between individuals have prevented us from achieving high drug efficacy in treating many complex diseases, including asthma. Genetics plays an important role in accounting for such interindividual variations in drug response. However, systematic approaches for addressing how genetic factors and their regulators determine variations in drug response in asthma treatment are lacking.
We sought to identify key transcriptional regulators of corticosteroid response in asthma using a novel systems biology approach.
We used Passing Attributes between Networks for Data Assimilations (PANDA) to construct the gene regulatory networks associated with good responders and poor responders to inhaled corticosteroids based on a subset of 145 white children with asthma who participated in the Childhood Asthma Management Cohort. PANDA uses gene expression profiles and published relationships among genes, transcription factors (TFs), and proteins to construct the directed networks of TFs and genes. We assessed the differential connectivity between the gene regulatory network of good responders versus that of poor responders.
When compared with poor responders, the network of good responders has differential connectivity and distinct ontologies (eg, proapoptosis enriched in network of good responders and antiapoptosis enriched in network of poor responders). Many of the key hubs identified in conjunction with clinical response are also cellular response hubs. Functional validation demonstrated abrogation of differences in corticosteroid-treated cell viability following siRNA knockdown of 2 TFs and differential downstream expression between good responders and poor responders.
We have identified and validated multiple TFs influencing asthma treatment response. Our results show that differential connectivity analysis can provide new insights into the heterogeneity of drug treatment effects.
个体之间药物反应的差异导致我们无法在治疗许多复杂疾病（包括哮喘）方面取得高疗效。遗传因素在解释药物反应的个体差异方面起着重要作用。然而，缺乏系统的方法来解决遗传因素及其调控因子如何决定哮喘治疗中药物反应的变化。
我们试图使用一种新的系统生物学方法来确定哮喘皮质类固醇反应的关键转录调控因子。
我们使用网络之间传递属性进行数据同化（PANDA），根据参与儿童哮喘管理队列的 145 名白人儿童的子集，构建与吸入皮质类固醇的良好反应者和不良反应者相关的基因调控网络。PANDA 使用基因表达谱和已发表的基因、转录因子（TF）和蛋白质之间的关系来构建 TF 和基因的有向网络。我们评估了良好反应者的基因调控网络与不良反应者的基因调控网络之间的差异连接性。
与不良反应者相比，良好反应者的网络具有不同的连接性和不同的本体论（例如，在良好反应者的网络中富集了促凋亡，而在不良反应者的网络中富集了抗凋亡）。与临床反应相关的许多关键枢纽也是细胞反应枢纽。功能验证表明，在 siRNA 敲低 2 个 TF 后，皮质类固醇处理的细胞活力差异得到了消除，并且良好反应者和不良反应者之间的下游表达存在差异。
我们已经鉴定和验证了多个影响哮喘治疗反应的 TF。我们的结果表明，差异连接性分析可以为药物治疗效果的异质性提供新的见解。
